from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase = None						,      ):



						"""simple docstring"""





						super().__init__()
						self.register_modules(transformer=lowercase						,      vae=lowercase						,      scheduler=lowercase   )

						# create a imagenet -> id dictionary for easier use
						A_						:							str      = {}
						if idalabel is not None:
								for key, value in idalabel.items():
										for label in value.split(','   ):
												A_						:							List[str]      = int(lowercase   )
								A_						:							str      = dict(sorted(self.labels.items()   )   )
				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						if not isinstance(lowercase						,      lowercase   ):
								A_						:							Any      = list(lowercase   )

						for l in label:
								if l not in self.labels:
										raise ValueError(
										    F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''   )

						return [self.labels[l] for l in label]







				@torch.no_grad()
				def __call__(		self						,      lowercase						,      lowercase = 4.0						,      lowercase = None						,      lowercase = 5_0						,      lowercase = "pil"						,      lowercase = True						,      ):



						"""simple docstring"""





						A_						:							Tuple      = len(lowercase   )
						A_						:							Optional[int]      = self.transformer.config.sample_size
						A_						:							List[Any]      = self.transformer.config.in_channels

						A_						:							Optional[int]      = randn_tensor(
						    shape=(batch_size, latent_channels, latent_size, latent_size)						,      generator=lowercase						,      device=self.device						,      dtype=self.transformer.dtype						,      )
						A_						:							Dict      = torch.cat([latents] * 2   ) if guidance_scale > 1 else latents

						A_						:							Dict      = torch.tensor(lowercase						,      device=self.device   ).reshape(-1   )
						A_						:							List[str]      = torch.tensor([1_0_0_0] * batch_size						,      device=self.device   )
						A_						:							str      = torch.cat([class_labels, class_null]						,      0   ) if guidance_scale > 1 else class_labels

						# set step values
						self.scheduler.set_timesteps(lowercase   )

						for t in self.progress_bar(self.scheduler.timesteps   ):
								if guidance_scale > 1:
										A_						:							Dict      = latent_model_input[: len(lowercase   ) // 2]
										A_						:							Tuple      = torch.cat([half, half]						,      dim=0   )
								A_						:							List[Any]      = self.scheduler.scale_model_input(lowercase						,      lowercase   )

								A_						:							List[Any]      = t
								if not torch.is_tensor(lowercase   ):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										A_						:							Any      = latent_model_input.device.type == 'mps'
										if isinstance(lowercase						,      lowercase   ):
												A_						:							Dict      = torch.floataa if is_mps else torch.floataa
										else:
												A_						:							Dict      = torch.intaa if is_mps else torch.intaa
										A_						:							Any      = torch.tensor([timesteps]						,      dtype=lowercase						,      device=latent_model_input.device   )
								elif len(timesteps.shape   ) == 0:
										A_						:							Optional[int]      = timesteps[None].to(latent_model_input.device   )
								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								A_						:							List[Any]      = timesteps.expand(latent_model_input.shape[0]   )
								# predict noise model_output
								A_						:							Tuple      = self.transformer(
								    lowercase						,      timestep=lowercase						,      class_labels=lowercase   ).sample

								# perform guidance
								if guidance_scale > 1:
										A_  ,     A_						:							Dict      = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										A_  ,     A_						:							Any      = torch.split(lowercase						,      len(lowercase   ) // 2						,      dim=0   )

										A_						:							Dict      = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										A_						:							Dict      = torch.cat([half_eps, half_eps]						,      dim=0   )

										A_						:							Dict      = torch.cat([eps, rest]						,      dim=1   )

								# learned sigma
								if self.transformer.config.out_channels // 2 == latent_channels:
										A_  ,     A_						:							Dict      = torch.split(lowercase						,      lowercase						,      dim=1   )
								else:
										A_						:							Union[str, Any]      = noise_pred

								# compute previous image: x_t -> x_t-1
								A_						:							Tuple      = self.scheduler.step(lowercase						,      lowercase						,      lowercase   ).prev_sample

						if guidance_scale > 1:
								A_  ,     A_						:							List[str]      = latent_model_input.chunk(2						,      dim=0   )
						else:
								A_						:							Union[str, Any]      = latent_model_input

						A_						:							Dict      = 1 / self.vae.config.scaling_factor * latents
						A_						:							Any      = self.vae.decode(lowercase   ).sample

						A_						:							int      = (samples / 2 + 0.5).clamp(0						,      1   )

						# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
						A_						:							Union[str, Any]      = samples.cpu().permute(0						,      2						,      3						,      1   ).float().numpy()

						if output_type == "pil":
								A_						:							Dict      = self.numpy_to_pil(lowercase   )

						if not return_dict:
								return (samples,)

						return ImagePipelineOutput(images=lowercase   )







from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


_UpperCAmelCase          =						logging.get_logger(__name__)





class UpperCAmelCase		:


				'''simple docstring'''






				lowerCamelCase_        =   42
				lowerCamelCase_        =   None
				@staticmethod
				def     lowerCAmelCase_  (		):



						"""simple docstring"""





						raise NotImplementedError
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						raise NotImplementedError
				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						raise NotImplementedError
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						if not self.is_available():
								raise RuntimeError(
								    F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''   )







				@classmethod
				def     lowerCAmelCase_  (		cls   ):



						"""simple docstring"""





						return F'''`pip install {cls.pip_package or cls.name}`'''





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''optuna'''
				@staticmethod
				def     lowerCAmelCase_  (		):



						"""simple docstring"""





						return is_optuna_available()
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						return run_hp_search_optuna(lowercase						,      lowercase						,      lowercase						,      **lowercase   )







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						return default_hp_space_optuna(lowercase   )





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''ray'''
				lowerCamelCase_        =   '''\'ray[tune]\''''
				@staticmethod
				def     lowerCAmelCase_  (		):



						"""simple docstring"""





						return is_ray_available()
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						return run_hp_search_ray(lowercase						,      lowercase						,      lowercase						,      **lowercase   )







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						return default_hp_space_ray(lowercase   )





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''sigopt'''
				@staticmethod
				def     lowerCAmelCase_  (		):



						"""simple docstring"""





						return is_sigopt_available()
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						return run_hp_search_sigopt(lowercase						,      lowercase						,      lowercase						,      **lowercase   )







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						return default_hp_space_sigopt(lowercase   )





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''wandb'''
				@staticmethod
				def     lowerCAmelCase_  (		):



						"""simple docstring"""





						return is_wandb_available()
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						return run_hp_search_wandb(lowercase						,      lowercase						,      lowercase						,      **lowercase   )







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						return default_hp_space_wandb(lowercase   )

_UpperCAmelCase          =						{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}




def 							UpperCamelCase		(			):
		'''simple docstring'''




		A_						:							List[str]      = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
		if len(__lowercase      ) > 0:
				A_						:							List[str]      = available_backends[0].name
				if len(__lowercase      ) > 1:
						logger.info(
						    f'''{len(__lowercase      )} hyperparameter search backends available. Using {name} as the default.'''      )
				return name
		raise RuntimeError(
		    'No hyperparameter search backend available.\n'
		    + '\n'.join(
		        f''' - To install {backend.name} run {backend.pip_install()}'''
		        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()      )      )






